interfaces.semtools.utilities.brains

BRAINSAlignMSP

Link to code

Wraps the executable command `` BRAINSAlignMSP ``.

title: Align Mid Saggital Brain (BRAINS)

category: Utilities.BRAINS

description: Resample an image into ACPC alignement ACPCDetect

Inputs:

[Optional]
inputVolume: (an existing file name)
        , The Image to be resampled,
        argument: ``--inputVolume %s``
OutputresampleMSP: (a boolean or a file name)
        , The image to be output.,
        argument: ``--OutputresampleMSP %s``
verbose: (a boolean)
        , Show more verbose output,
        argument: ``--verbose ``
resultsDir: (a boolean or a directory name)
        , The directory for the results to be written.,
        argument: ``--resultsDir %s``
writedebuggingImagesLevel: (an integer (int or long))
        , This flag controls if debugging images are produced. By default
        value of 0 is no images. Anything greater than zero will be
        increasing level of debugging images.,
        argument: ``--writedebuggingImagesLevel %d``
mspQualityLevel: (an integer (int or long))
        , Flag cotrols how agressive the MSP is estimated. 0=quick estimate
        (9 seconds), 1=normal estimate (11 seconds), 2=great estimate (22
        seconds), 3=best estimate (58 seconds).,
        argument: ``--mspQualityLevel %d``
rescaleIntensities: (a boolean)
        , Flag to turn on rescaling image intensities on input.,
        argument: ``--rescaleIntensities ``
trimRescaledIntensities: (a float)
        , Turn on clipping the rescaled image one-tailed on input. Units of
        standard deviations above the mean. Very large values are very
        permissive. Non-positive value turns clipping off. Defaults to
        removing 0.00001 of a normal tail above the mean.,
        argument: ``--trimRescaledIntensities %f``
rescaleIntensitiesOutputRange: (a list of items which are an integer
          (int or long))
        , This pair of integers gives the lower and upper bounds on the
        signal portion of the output image. Out-of-field voxels are taken
        from BackgroundFillValue.,
        argument: ``--rescaleIntensitiesOutputRange %s``
BackgroundFillValue: (a unicode string)
        Fill the background of image with specified short int value. Enter
        number or use BIGNEG for a large negative number.
        argument: ``--BackgroundFillValue %s``
interpolationMode: ('NearestNeighbor' or 'Linear' or
          'ResampleInPlace' or 'BSpline' or 'WindowedSinc' or 'Hamming' or
          'Cosine' or 'Welch' or 'Lanczos' or 'Blackman')
        Type of interpolation to be used when applying transform to moving
        volume. Options are Linear, ResampleInPlace, NearestNeighbor,
        BSpline, or WindowedSinc
        argument: ``--interpolationMode %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

OutputresampleMSP: (an existing file name)
        , The image to be output.,
resultsDir: (an existing directory name)
        , The directory for the results to be written.,

BRAINSClipInferior

Link to code

Wraps the executable command `` BRAINSClipInferior ``.

title: Clip Inferior of Center of Brain (BRAINS)

category: Utilities.BRAINS

description: This program will read the inputVolume as a short int image, write the BackgroundFillValue everywhere inferior to the lower bound, and write the resulting clipped short int image in the outputVolume.

version: 1.0

Inputs:

[Optional]
inputVolume: (an existing file name)
        Input image to make a clipped short int copy from.
        argument: ``--inputVolume %s``
outputVolume: (a boolean or a file name)
        Output image, a short int copy of the upper portion of the input
        image, filled with BackgroundFillValue.
        argument: ``--outputVolume %s``
acLowerBound: (a float)
        , When the input image to the output image, replace the image with
        the BackgroundFillValue everywhere below the plane This Far in
        physical units (millimeters) below (inferior to) the AC point
        (assumed to be the voxel field middle.) The oversize default was
        chosen to have no effect. Based on visualizing a thousand masks in
        the IPIG study, we recommend a limit no smaller than 80.0 mm.,
        argument: ``--acLowerBound %f``
BackgroundFillValue: (a unicode string)
        Fill the background of image with specified short int value. Enter
        number or use BIGNEG for a large negative number.
        argument: ``--BackgroundFillValue %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputVolume: (an existing file name)
        Output image, a short int copy of the upper portion of the input
        image, filled with BackgroundFillValue.

BRAINSConstellationModeler

Link to code

Wraps the executable command `` BRAINSConstellationModeler ``.

title: Generate Landmarks Model (BRAINS)

category: Utilities.BRAINS

description: Train up a model for BRAINSConstellationDetector

Inputs:

[Optional]
verbose: (a boolean)
        , Show more verbose output,
        argument: ``--verbose ``
inputTrainingList: (an existing file name)
        , Setup file, giving all parameters for training up a template model
        for each landmark.,
        argument: ``--inputTrainingList %s``
outputModel: (a boolean or a file name)
        , The full filename of the output model file.,
        argument: ``--outputModel %s``
saveOptimizedLandmarks: (a boolean)
        , Flag to make a new subject-specific landmark definition file in
        the same format produced by Slicer3 with the optimized landmark (the
        detected RP, AC, and PC) in it. Useful to tighten the variances in
        the ConstellationModeler.,
        argument: ``--saveOptimizedLandmarks ``
optimizedLandmarksFilenameExtender: (a unicode string)
        , If the trainingList is (indexFullPathName) and contains landmark
        data filenames [path]/[filename].fcsv , make the optimized landmarks
        filenames out of [path]/[filename](thisExtender) and the optimized
        version of the input trainingList out of
        (indexFullPathName)(thisExtender) , when you rewrite all the
        landmarks according to the saveOptimizedLandmarks flag.,
        argument: ``--optimizedLandmarksFilenameExtender %s``
resultsDir: (a boolean or a directory name)
        , The directory for the results to be written.,
        argument: ``--resultsDir %s``
mspQualityLevel: (an integer (int or long))
        , Flag cotrols how agressive the MSP is estimated. 0=quick estimate
        (9 seconds), 1=normal estimate (11 seconds), 2=great estimate (22
        seconds), 3=best estimate (58 seconds).,
        argument: ``--mspQualityLevel %d``
rescaleIntensities: (a boolean)
        , Flag to turn on rescaling image intensities on input.,
        argument: ``--rescaleIntensities ``
trimRescaledIntensities: (a float)
        , Turn on clipping the rescaled image one-tailed on input. Units of
        standard deviations above the mean. Very large values are very
        permissive. Non-positive value turns clipping off. Defaults to
        removing 0.00001 of a normal tail above the mean.,
        argument: ``--trimRescaledIntensities %f``
rescaleIntensitiesOutputRange: (a list of items which are an integer
          (int or long))
        , This pair of integers gives the lower and upper bounds on the
        signal portion of the output image. Out-of-field voxels are taken
        from BackgroundFillValue.,
        argument: ``--rescaleIntensitiesOutputRange %s``
BackgroundFillValue: (a unicode string)
        Fill the background of image with specified short int value. Enter
        number or use BIGNEG for a large negative number.
        argument: ``--BackgroundFillValue %s``
writedebuggingImagesLevel: (an integer (int or long))
        , This flag controls if debugging images are produced. By default
        value of 0 is no images. Anything greater than zero will be
        increasing level of debugging images.,
        argument: ``--writedebuggingImagesLevel %d``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputModel: (an existing file name)
        , The full filename of the output model file.,
resultsDir: (an existing directory name)
        , The directory for the results to be written.,

BRAINSEyeDetector

Link to code

Wraps the executable command `` BRAINSEyeDetector ``.

title: Eye Detector (BRAINS)

category: Utilities.BRAINS

version: 1.0

documentation-url: http://www.nitrc.org/projects/brainscdetector/

Inputs:

[Optional]
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
inputVolume: (an existing file name)
        The input volume
        argument: ``--inputVolume %s``
outputVolume: (a boolean or a file name)
        The output volume
        argument: ``--outputVolume %s``
debugDir: (a unicode string)
        A place for debug information
        argument: ``--debugDir %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputVolume: (an existing file name)
        The output volume

BRAINSInitializedControlPoints

Link to code

Wraps the executable command `` BRAINSInitializedControlPoints ``.

title: Initialized Control Points (BRAINS)

category: Utilities.BRAINS

description: Outputs bspline control points as landmarks

version: 0.1.0.$Revision: 916 $(alpha)

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Mark Scully

acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Additional support for Mark Scully and Hans Johnson at the University of Iowa.

Inputs:

[Optional]
inputVolume: (an existing file name)
        Input Volume
        argument: ``--inputVolume %s``
outputVolume: (a boolean or a file name)
        Output Volume
        argument: ``--outputVolume %s``
splineGridSize: (a list of items which are an integer (int or long))
        The number of subdivisions of the BSpline Grid to be centered on the
        image space. Each dimension must have at least 3 subdivisions for
        the BSpline to be correctly computed.
        argument: ``--splineGridSize %s``
permuteOrder: (a list of items which are an integer (int or long))
        The permutation order for the images. The default is 0,1,2 (i.e. no
        permutation)
        argument: ``--permuteOrder %s``
outputLandmarksFile: (a unicode string)
        Output filename
        argument: ``--outputLandmarksFile %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputVolume: (an existing file name)
        Output Volume

BRAINSLandmarkInitializer

Link to code

Wraps the executable command `` BRAINSLandmarkInitializer ``.

title: BRAINSLandmarkInitializer

category: Utilities.BRAINS

description: Create transformation file (*mat) from a pair of landmarks (*fcsv) files.

version: 1.0

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Eunyoung Regina Kim

Inputs:

[Optional]
inputFixedLandmarkFilename: (an existing file name)
        input fixed landmark. *.fcsv
        argument: ``--inputFixedLandmarkFilename %s``
inputMovingLandmarkFilename: (an existing file name)
        input moving landmark. *.fcsv
        argument: ``--inputMovingLandmarkFilename %s``
inputWeightFilename: (an existing file name)
        Input weight file name for landmarks. Higher weighted landmark will
        be considered more heavily. Weights are propotional, that is the
        magnitude of weights will be normalized by its minimum and maximum
        value.
        argument: ``--inputWeightFilename %s``
outputTransformFilename: (a boolean or a file name)
        output transform file name (ex: ./outputTransform.mat)
        argument: ``--outputTransformFilename %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputTransformFilename: (an existing file name)
        output transform file name (ex: ./outputTransform.mat)

BRAINSLinearModelerEPCA

Link to code

Wraps the executable command `` BRAINSLinearModelerEPCA ``.

title: Landmark Linear Modeler (BRAINS)

category: Utilities.BRAINS

description: Training linear model using EPCA. Implementation based on my MS thesis, “A METHOD FOR AUTOMATED LANDMARK CONSTELLATION DETECTION USING EVOLUTIONARY PRINCIPAL COMPONENTS AND STATISTICAL SHAPE MODELS”

version: 1.0

documentation-url: http://www.nitrc.org/projects/brainscdetector/

Inputs:

[Optional]
inputTrainingList: (an existing file name)
        Input Training Landmark List Filename,
        argument: ``--inputTrainingList %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

None

BRAINSLmkTransform

Link to code

Wraps the executable command `` BRAINSLmkTransform ``.

title: Landmark Transform (BRAINS)

category: Utilities.BRAINS

description: This utility program estimates the affine transform to align the fixed landmarks to the moving landmarks, and then generate the resampled moving image to the same physical space as that of the reference image.

version: 1.0

documentation-url: http://www.nitrc.org/projects/brainscdetector/

Inputs:

[Optional]
inputMovingLandmarks: (an existing file name)
        Input Moving Landmark list file in fcsv,
        argument: ``--inputMovingLandmarks %s``
inputFixedLandmarks: (an existing file name)
        Input Fixed Landmark list file in fcsv,
        argument: ``--inputFixedLandmarks %s``
outputAffineTransform: (a boolean or a file name)
        The filename for the estimated affine transform,
        argument: ``--outputAffineTransform %s``
inputMovingVolume: (an existing file name)
        The filename of input moving volume
        argument: ``--inputMovingVolume %s``
inputReferenceVolume: (an existing file name)
        The filename of the reference volume
        argument: ``--inputReferenceVolume %s``
outputResampledVolume: (a boolean or a file name)
        The filename of the output resampled volume
        argument: ``--outputResampledVolume %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputAffineTransform: (an existing file name)
        The filename for the estimated affine transform,
outputResampledVolume: (an existing file name)
        The filename of the output resampled volume

BRAINSMush

Link to code

Wraps the executable command `` BRAINSMush ``.

title: Brain Extraction from T1/T2 image (BRAINS)

category: Utilities.BRAINS

description: This program: 1) generates a weighted mixture image optimizing the mean and variance and 2) produces a mask of the brain volume

version: 0.1.0.$Revision: 1.4 $(alpha)

documentation-url: http:://mri.radiology.uiowa.edu

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: This tool is a modification by Steven Dunn of a program developed by Greg Harris and Ron Pierson.

acknowledgements: This work was developed by the University of Iowa Departments of Radiology and Psychiatry. This software was supported in part of NIH/NINDS award NS050568.

Inputs:

[Optional]
inputFirstVolume: (an existing file name)
        Input image (1) for mixture optimization
        argument: ``--inputFirstVolume %s``
inputSecondVolume: (an existing file name)
        Input image (2) for mixture optimization
        argument: ``--inputSecondVolume %s``
inputMaskVolume: (an existing file name)
        Input label image for mixture optimization
        argument: ``--inputMaskVolume %s``
outputWeightsFile: (a boolean or a file name)
        Output Weights File
        argument: ``--outputWeightsFile %s``
outputVolume: (a boolean or a file name)
        The MUSH image produced from the T1 and T2 weighted images
        argument: ``--outputVolume %s``
outputMask: (a boolean or a file name)
        The brain volume mask generated from the MUSH image
        argument: ``--outputMask %s``
seed: (a list of items which are an integer (int or long))
        Seed Point for Brain Region Filling
        argument: ``--seed %s``
desiredMean: (a float)
        Desired mean within the mask for weighted sum of both images.
        argument: ``--desiredMean %f``
desiredVariance: (a float)
        Desired variance within the mask for weighted sum of both images.
        argument: ``--desiredVariance %f``
lowerThresholdFactorPre: (a float)
        Lower threshold factor for finding an initial brain mask
        argument: ``--lowerThresholdFactorPre %f``
upperThresholdFactorPre: (a float)
        Upper threshold factor for finding an initial brain mask
        argument: ``--upperThresholdFactorPre %f``
lowerThresholdFactor: (a float)
        Lower threshold factor for defining the brain mask
        argument: ``--lowerThresholdFactor %f``
upperThresholdFactor: (a float)
        Upper threshold factor for defining the brain mask
        argument: ``--upperThresholdFactor %f``
boundingBoxSize: (a list of items which are an integer (int or long))
        Size of the cubic bounding box mask used when no brain mask is
        present
        argument: ``--boundingBoxSize %s``
boundingBoxStart: (a list of items which are an integer (int or
          long))
        XYZ point-coordinate for the start of the cubic bounding box mask
        used when no brain mask is present
        argument: ``--boundingBoxStart %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputWeightsFile: (an existing file name)
        Output Weights File
outputVolume: (an existing file name)
        The MUSH image produced from the T1 and T2 weighted images
outputMask: (an existing file name)
        The brain volume mask generated from the MUSH image

BRAINSSnapShotWriter

Link to code

Wraps the executable command `` BRAINSSnapShotWriter ``.

title: BRAINSSnapShotWriter

category: Utilities.BRAINS

description: Create 2D snapshot of input images. Mask images are color-coded

version: 1.0

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Eunyoung Regina Kim

Inputs:

[Optional]
inputVolumes: (a list of items which are an existing file name)
        Input image volume list to be extracted as 2D image. Multiple input
        is possible. At least one input is required.
        argument: ``--inputVolumes %s...``
inputBinaryVolumes: (a list of items which are an existing file name)
        Input mask (binary) volume list to be extracted as 2D image.
        Multiple input is possible.
        argument: ``--inputBinaryVolumes %s...``
inputSliceToExtractInPhysicalPoint: (a list of items which are a
          float)
        2D slice number of input images. For autoWorkUp output, which AC-PC
        aligned, 0,0,0 will be the center.
        argument: ``--inputSliceToExtractInPhysicalPoint %s``
inputSliceToExtractInIndex: (a list of items which are an integer
          (int or long))
        2D slice number of input images. For size of 256*256*256 image, 128
        is usually used.
        argument: ``--inputSliceToExtractInIndex %s``
inputSliceToExtractInPercent: (a list of items which are an integer
          (int or long))
        2D slice number of input images. Percentage input from 0%-100%. (ex.
        --inputSliceToExtractInPercent 50,50,50
        argument: ``--inputSliceToExtractInPercent %s``
inputPlaneDirection: (a list of items which are an integer (int or
          long))
        Plane to display. In general, 0=saggital, 1=coronal, and 2=axial
        plane.
        argument: ``--inputPlaneDirection %s``
outputFilename: (a boolean or a file name)
        2D file name of input images. Required.
        argument: ``--outputFilename %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputFilename: (an existing file name)
        2D file name of input images. Required.

BRAINSTransformConvert

Link to code

Wraps the executable command `` BRAINSTransformConvert ``.

title: BRAINS Transform Convert

category: Utilities.BRAINS

description: Convert ITK transforms to higher order transforms

version: 1.0

documentation-url: A utility to convert between transform file formats.

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Hans J. Johnson,Kent Williams, Ali Ghayoor

Inputs:

[Optional]
inputTransform: (an existing file name)
        argument: ``--inputTransform %s``
referenceVolume: (an existing file name)
        argument: ``--referenceVolume %s``
outputTransformType: ('Affine' or 'VersorRigid' or 'ScaleVersor' or
          'ScaleSkewVersor' or 'DisplacementField' or 'Same')
        The target transformation type. Must be conversion-compatible with
        the input transform type
        argument: ``--outputTransformType %s``
outputPrecisionType: ('double' or 'float')
        Precision type of the output transform. It can be either single
        precision or double precision
        argument: ``--outputPrecisionType %s``
displacementVolume: (a boolean or a file name)
        argument: ``--displacementVolume %s``
outputTransform: (a boolean or a file name)
        argument: ``--outputTransform %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

displacementVolume: (an existing file name)
outputTransform: (an existing file name)

BRAINSTrimForegroundInDirection

Link to code

Wraps the executable command `` BRAINSTrimForegroundInDirection ``.

title: Trim Foreground In Direction (BRAINS)

category: Utilities.BRAINS

description: This program will trim off the neck and also air-filling noise from the inputImage.

version: 0.1

documentation-url: http://www.nitrc.org/projects/art/

Inputs:

[Optional]
inputVolume: (an existing file name)
        Input image to trim off the neck (and also air-filling noise.)
        argument: ``--inputVolume %s``
outputVolume: (a boolean or a file name)
        Output image with neck and air-filling noise trimmed isotropic image
        with AC at center of image.
        argument: ``--outputVolume %s``
directionCode: (an integer (int or long))
        , This flag chooses which dimension to compare. The sign lets you
        flip direction.,
        argument: ``--directionCode %d``
otsuPercentileThreshold: (a float)
        , This is a parameter to FindLargestForegroundFilledMask, which is
        employed to trim off air-filling noise.,
        argument: ``--otsuPercentileThreshold %f``
closingSize: (an integer (int or long))
        , This is a parameter to FindLargestForegroundFilledMask,
        argument: ``--closingSize %d``
headSizeLimit: (a float)
        , Use this to vary from the command line our search for how much
        upper tissue is head for the center-of-mass calculation. Units are
        CCs, not cubic millimeters.,
        argument: ``--headSizeLimit %f``
BackgroundFillValue: (a unicode string)
        Fill the background of image with specified short int value. Enter
        number or use BIGNEG for a large negative number.
        argument: ``--BackgroundFillValue %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputVolume: (an existing file name)
        Output image with neck and air-filling noise trimmed isotropic image
        with AC at center of image.

CleanUpOverlapLabels

Link to code

Wraps the executable command `` CleanUpOverlapLabels ``.

title: Clean Up Overla Labels

category: Utilities.BRAINS

description: Take a series of input binary images and clean up for those overlapped area. Binary volumes given first always wins out

version: 0.1.0

contributor: Eun Young Kim

Inputs:

[Optional]
inputBinaryVolumes: (a list of items which are an existing file name)
        The list of binary images to be checked and cleaned up. Order is
        important. Binary volume given first always wins out.
        argument: ``--inputBinaryVolumes %s...``
outputBinaryVolumes: (a boolean or a list of items which are a file
          name)
        The output label map images, with integer values in it. Each label
        value specified in the inputLabels is combined into this output
        label map volume
        argument: ``--outputBinaryVolumes %s...``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputBinaryVolumes: (a list of items which are an existing file
          name)
        The output label map images, with integer values in it. Each label
        value specified in the inputLabels is combined into this output
        label map volume

FindCenterOfBrain

Link to code

Wraps the executable command `` FindCenterOfBrain ``.

title: Center Of Brain (BRAINS)

category: Utilities.BRAINS

description: Finds the center point of a brain

version: 3.0.0

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Hans J. Johnson, hans-johnson -at- uiowa.edu, http://wwww.psychiatry.uiowa.edu

acknowledgements: Hans Johnson(1,3,4); Kent Williams(1); (1=University of Iowa Department of Psychiatry, 3=University of Iowa Department of Biomedical Engineering, 4=University of Iowa Department of Electrical and Computer Engineering

Inputs:

[Optional]
inputVolume: (an existing file name)
        The image in which to find the center.
        argument: ``--inputVolume %s``
imageMask: (an existing file name)
        argument: ``--imageMask %s``
clippedImageMask: (a boolean or a file name)
        argument: ``--clippedImageMask %s``
maximize: (a boolean)
        argument: ``--maximize ``
axis: (an integer (int or long))
        argument: ``--axis %d``
otsuPercentileThreshold: (a float)
        argument: ``--otsuPercentileThreshold %f``
closingSize: (an integer (int or long))
        argument: ``--closingSize %d``
headSizeLimit: (a float)
        argument: ``--headSizeLimit %f``
headSizeEstimate: (a float)
        argument: ``--headSizeEstimate %f``
backgroundValue: (an integer (int or long))
        argument: ``--backgroundValue %d``
generateDebugImages: (a boolean)
        argument: ``--generateDebugImages ``
debugDistanceImage: (a boolean or a file name)
        argument: ``--debugDistanceImage %s``
debugGridImage: (a boolean or a file name)
        argument: ``--debugGridImage %s``
debugAfterGridComputationsForegroundImage: (a boolean or a file name)
        argument: ``--debugAfterGridComputationsForegroundImage %s``
debugClippedImageMask: (a boolean or a file name)
        argument: ``--debugClippedImageMask %s``
debugTrimmedImage: (a boolean or a file name)
        argument: ``--debugTrimmedImage %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

clippedImageMask: (an existing file name)
debugDistanceImage: (an existing file name)
debugGridImage: (an existing file name)
debugAfterGridComputationsForegroundImage: (an existing file name)
debugClippedImageMask: (an existing file name)
debugTrimmedImage: (an existing file name)

GenerateLabelMapFromProbabilityMap

Link to code

Wraps the executable command `` GenerateLabelMapFromProbabilityMap ``.

title: Label Map from Probability Images

category: Utilities.BRAINS

description: Given a list of probability maps for labels, create a discrete label map where only the highest probability region is used for the labeling.

version: 0.1

contributor: University of Iowa Department of Psychiatry, http:://www.psychiatry.uiowa.edu

Inputs:

[Optional]
inputVolumes: (a list of items which are an existing file name)
        The Input probaiblity images to be computed for lable maps
        argument: ``--inputVolumes %s...``
outputLabelVolume: (a boolean or a file name)
        The Input binary image for region of interest
        argument: ``--outputLabelVolume %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputLabelVolume: (an existing file name)
        The Input binary image for region of interest

ImageRegionPlotter

Link to code

Wraps the executable command `` ImageRegionPlotter ``.

title: Write Out Image Intensities

category: Utilities.BRAINS

description: For Analysis

version: 0.1

contributor: University of Iowa Department of Psychiatry, http:://www.psychiatry.uiowa.edu

Inputs:

[Optional]
inputVolume1: (an existing file name)
        The Input image to be computed for statistics
        argument: ``--inputVolume1 %s``
inputVolume2: (an existing file name)
        The Input image to be computed for statistics
        argument: ``--inputVolume2 %s``
inputBinaryROIVolume: (an existing file name)
        The Input binary image for region of interest
        argument: ``--inputBinaryROIVolume %s``
inputLabelVolume: (an existing file name)
        The Label Image
        argument: ``--inputLabelVolume %s``
numberOfHistogramBins: (an integer (int or long))
         the number of histogram levels
        argument: ``--numberOfHistogramBins %d``
outputJointHistogramData: (a unicode string)
         output data file name
        argument: ``--outputJointHistogramData %s``
useROIAUTO: (a boolean)
         Use ROIAUTO to compute region of interest. This cannot be used with
        inputLabelVolume
        argument: ``--useROIAUTO ``
useIntensityForHistogram: (a boolean)
         Create Intensity Joint Histogram instead of Quantile Joint
        Histogram
        argument: ``--useIntensityForHistogram ``
verbose: (a boolean)
         print debugging information,
        argument: ``--verbose ``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

None

JointHistogram

Link to code

Wraps the executable command `` JointHistogram ``.

title: Write Out Image Intensities

category: Utilities.BRAINS

description: For Analysis

version: 0.1

contributor: University of Iowa Department of Psychiatry, http:://www.psychiatry.uiowa.edu

Inputs:

[Optional]
inputVolumeInXAxis: (an existing file name)
        The Input image to be computed for statistics
        argument: ``--inputVolumeInXAxis %s``
inputVolumeInYAxis: (an existing file name)
        The Input image to be computed for statistics
        argument: ``--inputVolumeInYAxis %s``
inputMaskVolumeInXAxis: (an existing file name)
        Input mask volume for inputVolumeInXAxis. Histogram will be computed
        just for the masked region
        argument: ``--inputMaskVolumeInXAxis %s``
inputMaskVolumeInYAxis: (an existing file name)
        Input mask volume for inputVolumeInYAxis. Histogram will be computed
        just for the masked region
        argument: ``--inputMaskVolumeInYAxis %s``
outputJointHistogramImage: (a unicode string)
         output joint histogram image file name. Histogram is usually 2D
        image.
        argument: ``--outputJointHistogramImage %s``
verbose: (a boolean)
         print debugging information,
        argument: ``--verbose ``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

None

ShuffleVectorsModule

Link to code

Wraps the executable command `` ShuffleVectorsModule ``.

title: ShuffleVectors

category: Utilities.BRAINS

description: Automatic Segmentation using neural networks

version: 1.0

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Hans Johnson

Inputs:

[Optional]
inputVectorFileBaseName: (an existing file name)
        input vector file name prefix. Usually end with .txt and header file
        has prost fix of .txt.hdr
        argument: ``--inputVectorFileBaseName %s``
outputVectorFileBaseName: (a boolean or a file name)
        output vector file name prefix. Usually end with .txt and header
        file has prost fix of .txt.hdr
        argument: ``--outputVectorFileBaseName %s``
resampleProportion: (a float)
        downsample size of 1 will be the same size as the input images,
        downsample size of 3 will throw 2/3 the vectors away.
        argument: ``--resampleProportion %f``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputVectorFileBaseName: (an existing file name)
        output vector file name prefix. Usually end with .txt and header
        file has prost fix of .txt.hdr

fcsv_to_hdf5

Link to code

Wraps the executable command `` fcsv_to_hdf5 ``.

title: fcsv_to_hdf5 (BRAINS)

category: Utilities.BRAINS

description: Convert a collection of fcsv files to a HDF5 format file

Inputs:

[Optional]
versionID: (a unicode string)
        , Current version ID. It should be match with the version of BCD
        that will be using the output model file,
        argument: ``--versionID %s``
landmarksInformationFile: (a boolean or a file name)
        , name of HDF5 file to write matrices into,
        argument: ``--landmarksInformationFile %s``
landmarkTypesList: (an existing file name)
        , file containing list of landmark types,
        argument: ``--landmarkTypesList %s``
modelFile: (a boolean or a file name)
        , name of HDF5 file containing BRAINSConstellationDetector Model
        file (LLSMatrices, LLSMeans and LLSSearchRadii),
        argument: ``--modelFile %s``
landmarkGlobPattern: (a unicode string)
        Glob pattern to select fcsv files
        argument: ``--landmarkGlobPattern %s``
numberOfThreads: (an integer (int or long))
        Explicitly specify the maximum number of threads to use.
        argument: ``--numberOfThreads %d``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

landmarksInformationFile: (an existing file name)
        , name of HDF5 file to write matrices into,
modelFile: (an existing file name)
        , name of HDF5 file containing BRAINSConstellationDetector Model
        file (LLSMatrices, LLSMeans and LLSSearchRadii),

insertMidACPCpoint

Link to code

Wraps the executable command `` insertMidACPCpoint ``.

title: MidACPC Landmark Insertion

category: Utilities.BRAINS

description: This program gets a landmark fcsv file and adds a new landmark as the midpoint between AC and PC points to the output landmark fcsv file

contributor: Ali Ghayoor

Inputs:

[Optional]
inputLandmarkFile: (an existing file name)
        Input landmark file (.fcsv)
        argument: ``--inputLandmarkFile %s``
outputLandmarkFile: (a boolean or a file name)
        Output landmark file (.fcsv)
        argument: ``--outputLandmarkFile %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputLandmarkFile: (an existing file name)
        Output landmark file (.fcsv)

landmarksConstellationAligner

Link to code

Wraps the executable command `` landmarksConstellationAligner ``.

title: MidACPC Landmark Insertion

category: Utilities.BRAINS

description: This program converts the original landmark files to the acpc-aligned landmark files

contributor: Ali Ghayoor

Inputs:

[Optional]
inputLandmarksPaired: (an existing file name)
        Input landmark file (.fcsv)
        argument: ``--inputLandmarksPaired %s``
outputLandmarksPaired: (a boolean or a file name)
        Output landmark file (.fcsv)
        argument: ``--outputLandmarksPaired %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputLandmarksPaired: (an existing file name)
        Output landmark file (.fcsv)

landmarksConstellationWeights

Link to code

Wraps the executable command `` landmarksConstellationWeights ``.

title: Generate Landmarks Weights (BRAINS)

category: Utilities.BRAINS

description: Train up a list of Weights for the Landmarks in BRAINSConstellationDetector

Inputs:

[Optional]
inputTrainingList: (an existing file name)
        , Setup file, giving all parameters for training up a Weight list
        for landmark.,
        argument: ``--inputTrainingList %s``
inputTemplateModel: (an existing file name)
        User-specified template model.,
        argument: ``--inputTemplateModel %s``
LLSModel: (an existing file name)
        Linear least squares model filename in HD5 format
        argument: ``--LLSModel %s``
outputWeightsList: (a boolean or a file name)
        , The filename of a csv file which is a list of landmarks and their
        corresponding weights.,
        argument: ``--outputWeightsList %s``
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

outputWeightsList: (an existing file name)
        , The filename of a csv file which is a list of landmarks and their
        corresponding weights.,